def load(
refresh,
bq_read,
sqlfn="../fis/data/hist_data_proto.sql",
dest="/tmp/hists.pq",
):
if refresh:
sql = read(sqlfn)
df_ = bq_read(sql)
hist_cols = get_hist_cols_raw_dl(df_)
h_kw = {
h: lambda df, h=h: df[h].map(arr_of_str2dict)
for h in hist_cols
}
df_ = df_.assign(**h_kw)
# hist_cols = get_hist_cols(df)
hist_cols_asn = {
c: lambda x, c=c: x[c].map(z.keymap(str))
for c in hist_cols
}
ds = df_.assign(**hist_cols_asn)
ds.to_parquet(dest)
ds = pd.read_parquet(dest)
# print(ds.cycle_collector)
# return ds
fn = z.compose(typed_dict, z.keymap(int),
z.valfilter(lambda x: x is not None))
hist_cols = get_dict_hist_cols(ds)
hist_cols_asn = {c: lambda df, c=c: df[c].map(fn) for c in hist_cols}
# return ds
df = ds.assign(**hist_cols_asn)
return df
def _switch_models():
# Model switching (section 3.4)
print "Switching model params..."
g_params = tz.keymap(lambda n: ".".join(n.split(".")[1:]),
dict(G.enc.named_parameters()))
d_params = tz.keymap(lambda n: ".".join(n.split(".")[1:]),
dict(D.named_parameters()))
for name, param in g_params.iteritems():
d_params[name].data.copy_(param.data)
print "Model params switched!"
def vega2to3(data):
"""Transform a Vega data list from version 2 to 3.
Args:
data: vega data list
Returns:
update vega data list
"""
def keymapping(key):
"""Map vega data keys from version 2 to 3
The mapping is `test` -> `expr` and `field` -> `as` otherwise
the input key is just returned.
Args:
key: the key to map
Returns:
a new key
"""
return dict(test='expr', field='as').get(key, key)
update_transform = fcompose(map(keymap(keymapping)), list)
return pipe(
data,
map(update_in(keys=['transform'], func=update_transform, default=[])),
list)
def get_values(_type):
fields = list(map(lambda x: "{}_{}".format(_type, x), params))
_get = compose(
valmap(lambda x: x or 0),
keymap(lambda x: x.replace("{}_".format(_type), "")),
keyfilter(lambda x: x in fields),
)
return _get(data)
def annodize(name="__main__", annotations=None):
globals, locals = map(vars, map(importlib.import_module, [name] * 2))
__annotations__ = globals.get("__annotations__", {})
annotations = toolz.keymap(Forward, (annotations or {}))
annotations = toolz.keymap(lambda x: typing._eval_type(x, globals, locals),
annotations)
for key, value in __annotations__.items():
if getattr(value, "__forward_coerce__", False) is True:
value.__forward_coerce__ = key
if value in annotations:
new = copy.copy(annotations[value])
if getattr(new, "__forward_coerce__", False) is True:
new.__forward_coerce__ = key
__annotations__[key] = typing.Union[value, new]
typing._eval_type(__annotations__[key], globals, locals)
def graph_descriptors_np(data, delta_x=1.0, periodic_boundary=True):
"""Numpy only version of graph_descriptors function
Args:
data (array): array of phases ``(n_samples, n_x, n_y)``, values must be 0 or 1
delta_x (float): pixel size
periodic_boundary (bool): whether the boundaries are periodic
Returns:
A Pandas data frame with samples along rows and descriptors
along columns
"""
# pylint: enable=line-too-long
columns = keymap(
lambda x: x.encode("UTF-8"),
dict(
STAT_n="n_vertices",
STAT_e="n_edges",
STAT_n_D="n_phase0",
STAT_n_A="n_phase1",
STAT_CC_D="n_phase0_connect",
STAT_CC_A="n_phase1_connect",
STAT_CC_D_An="n_phase0_connect_top",
STAT_CC_A_Ca="n_phase1_connect_bottom",
ABS_wf_D="w_frac_phase0",
ABS_f_D="frac_phase0",
DISS_wf10_D="w_frac_phase0_10_dist",
DISS_f10_D="fraction_phase0_10_dist",
DISS_f2_D="fraction_phase0_2_dist",
CT_f_conn_D="frac_useful",
CT_f_e_conn="inter_frac_bottom_and_top",
CT_f_conn_D_An="frac_phase0_top",
CT_f_conn_A_Ca="frac_phase1_bottom",
CT_e_conn="n_inter_paths",
CT_e_D_An="n_phase0_inter_top",
CT_e_A_Ca="n_phase1_inter_bottom",
CT_f_D_tort1="frac_phase0_rising",
CT_f_A_tort1="frac_phase1_rising",
CT_n_D_adj_An="n_phase0_connect_anode",
CT_n_A_adj_Ca="n_phase1_connect_cathode",
),
)
return pipe(
data,
fmap(
graph_descriptors_sample(delta_x=delta_x,
periodic_boundary=periodic_boundary)),
list,
# debug('check fields in graspi'),
pandas.DataFrame,
lambda x: x.rename(columns=columns),
lambda x: x.apply(lambda x: np.rint(x).astype(int)
if x.name[:2] == "n_" else x),
)
def to_ascii_table(self):
table_data = tuple(
map(
flatten(1),
sorted(
valmap(', '.join, keymap(flatten(float('inf')),
self.dict)).items())))
header = [
[x if isinstance(x, string_types) else '' for x in self.levels] + [
'',
],
]
table = AsciiTable(header + list(map(list, table_data)),
'Collection: %s' % self.name)
return table
get_neighbors(current))
return map(
toolz.first,
graph_traverse(source=(source, 0),
get_neighbors=get_neighbors_limiting_radius),
)
edges_to_graph = toolz.compose(
curried.valmap(toolz.compose(frozenset, curried.map(toolz.second))),
curried.groupby(toolz.first),
)
graph_to_edges = toolz.compose_left(
curried.keymap(lambda x: (x, )),
dict.items,
curried.mapcat(functional.star(itertools.product)),
)
reverse_graph = toolz.compose_left(
graph_to_edges, curried.map(toolz.compose_left(reversed, tuple)),
edges_to_graph)
cliques_to_graph = toolz.compose_left(
curried.mapcat(lambda clique: itertools.permutations(clique, r=2)),
edges_to_graph)
def get_connectivity_components(graph: Dict) -> Iterable[FrozenSet]:
"""Graph is assumed to undirected, so each edge must appear both ways."""
def sym_inverse_transform_label_encoder(estimator):
mapping = keymap(
as_value,
dict(enumerate(map(compose(as_value, np_to_py), estimator.classes_))))
arg = StringVariable('x')
return Function.from_expression(FiniteMap(mapping=mapping, arg=arg))
from datetime import datetime
from tables import db, DelWordTable
from toolz.curried import map, filter, pipe, groupby, keymap
if __name__ == "__main__":
if len(sys.argv) != 2:
print("USAGE: python3 add_del_words.py words.txt")
sys.exit(1)
_, words_path = sys.argv
exist_wordphones = pipe(DelWordTable.select(), map(lambda e: e.word), set)
with open(words_path, "r", encoding='utf8') as fin:
to_add_words = pipe(
fin,
map(lambda e: e.strip()),
filter(lambda e: e != ''),
filter(lambda e: e not in exist_wordphones),
groupby(lambda e: e),
keymap(lambda e: DelWordTable(word=e, updatedt=datetime.now())),
)
with db.atomic():
DelWordTable.bulk_create(to_add_words, batch_size=100)
# for w in to_add_words:
# print(f"add {w}")
# # w.save()
print('done')
[
("Alice", "NYC"),
("Alice", "Chicago"),
("Dan", "Syndey"),
("Edith", "Paris"),
("Edith", "Berlin"),
("Zhao", "Shanghai"),
],
),
"count_by": (curried.countby(lambda x: x % 2 == 0), range(20)),
"groupby": (
chained(curried.groupby(lambda x: x % 2 == 0), curried.valmap(sorted)),
range(20),
),
"keymap": (
chained(dict, curried.keymap(lambda x: 2 * x)),
dict.items({
1: 2,
3: 4,
5: 6,
7: 8,
9: 10
}),
),
"valmap": (
chained(dict, curried.valmap(lambda x: 2 * x)),
dict.items({
1: 2,
3: 4,
5: 6,
7: 8,
